Robot routing on-line control method oriented to dynamic environments

Abstract

The invention discloses a robot routing on-line control method oriented to dynamic environments. The method includes the steps that 1, environment and parameter initialization is carried out; 2, a basic motion set of a robot and an abstract motion set form selectable motions of the robot, and the assessed values of all states-motions are initialized, wherein each abstract motion in the abstract motion set is an interruptable abstract motion; 3, random obstacles are generated; 4, a motion is selected through a motion selection module and an updating module, and the assessed value of the corresponding state-motion is updated; 5, if the selected motion reaches an endpoint state, the step 6 is executed, and otherwise, the step 4 is executed; 6, if conditions are met, the task is ended, and otherwise, the step 3 is executed. According to the method, by introducing an interrupt mechanism, the robot can be controlled online to apply abstract motions to uncertain dynamic environments and adapt to dynamic environments flexibly, then the robot can complete tasks in complex dynamic environments, and thus the application environments can be expanded while efficiency is improved.

Description

technical field [0001] The invention relates to an on-line control method for robot path-finding, which belongs to the field of machine learning, and in particular to an on-line control method for robot path-finding that is oriented to a dynamic environment and can interrupt abstract actions. Background technique [0002] With the continuous expansion of the application field of robots, the tasks faced by robots are becoming more and more complex. Although many algorithms can pre-design and program the possible behaviors of robots, there are various uncertainties when faced with complex tasks and the environment that the robot will face cannot be fully detected and will change dynamically. Designers are often unable to make effective predictions about the dynamically changing environment in advance, so that they cannot reasonably control the actions and behaviors of the robot according to the actual situation of the environment. Therefore, it is particularly important to de...

AI Technical Summary

Problems solved by technology

Due to the limitation of the definition of time abstraction, when the abstract action is adopted by the robot, it cannot execute other actions in the middle of the execution of the abstract action. This definition greatly limits the application environment of the abstract action, especially the environment for the robot to perform tasks. Macro-Q learning becomes inefficient when the environment is dynamically changing

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